中文标题#
心电图足够吗？ 使用心电图进行肺栓塞的深度学习分类
英文标题#
Are ECGs enough? Deep learning classification of pulmonary embolism using electrocardiograms
中文摘要#
肺栓塞是院外心脏骤停的主要原因，需要快速诊断。 虽然计算机断层扫描肺动脉造影是标准的诊断工具，但并非在所有地方都能获得。 心电图是诊断多种心脏异常的重要工具，因为它成本低、速度快，并且在许多环境中都可用。 然而，公共心电图数据集的可用性，特别是针对肺栓塞的，是有限的，在实践中这些数据集往往较小，这使得优化学习策略变得至关重要。 在本研究中，我们研究了多个神经网络的性能，以评估各种方法的影响。 此外，我们检查了当使用迁移学习将从较大的心电图数据集（如 PTB-XL、CPSC18 和 MedalCare-XL）中学到的信息转移到一个更小、更具挑战性的肺栓塞数据集时，这些实践是否能增强模型的泛化能力。 通过利用迁移学习，我们分析了在有限数据上可以多大程度地提高学习效率和预测性能。 代码可在 https://github.com/joaodsmarques/Are-ECGs-enough-Deep-Learning-Classifiers 获取。
英文摘要#
Pulmonary embolism is a leading cause of out of hospital cardiac arrest that requires fast diagnosis. While computed tomography pulmonary angiography is the standard diagnostic tool, it is not always accessible. Electrocardiography is an essential tool for diagnosing multiple cardiac anomalies, as it is affordable, fast and available in many settings. However, the availability of public ECG datasets, specially for PE, is limited and, in practice, these datasets tend to be small, making it essential to optimize learning strategies. In this study, we investigate the performance of multiple neural networks in order to assess the impact of various approaches. Moreover, we check whether these practices enhance model generalization when transfer learning is used to translate information learned in larger ECG datasets, such as PTB-XL, CPSC18 and MedalCare-XL, to a smaller, more challenging dataset for PE. By leveraging transfer learning, we analyze the extent to which we can improve learning efficiency and predictive performance on limited data. Code available at https://github.com/joaodsmarques/Are-ECGs-enough-Deep-Learning-Classifiers .
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